In all situations, the outcome is undoubtedly dependent on the inputs provided. Hence the quality of data entering the warehouse directly impacts the quality of the information that comes out of it. To ensure the superior quality of data in the data warehouse, issues related to quality have to be tackled at all phases. ETL and Data Staging is one of the most crucial of all stages. It is a chief location for validating data quality from source or auditing and tracking relative data issues. Outlier Analysis plays a pivotal role in addressing these challenges. Marlabs’ Outlier Analysis methodology identifies outlier in data load stage at file level by meritoriously saving the response time, with no further delay in analysis report. Marlabs Outlier Solution has an upper edge since hierarchy drill reaches till the raw data to provide the best upshots. Marlabs provides Field Level maintenance with special characters, numbers in character field, length of column, range value, nulls, and value based support, while the File Level assistance takes into account the uniform index of the file size, record count, length of record, and count of column. Marlabs Outliers algorithm isolates the glitches using advanced statistical techniques and mechanisms in data warehousing framework. To know in detail about Marlabs Outliers Analysis and other Data Warehousing Solutions, visit http://www.marlabs.com/technologies/bi-analytics/offerings

1. Marlabs Outliers Analysis
Field Level
Special characters
Numbers in character field
Length of column
Max/Min value in column
Nulls
Value based
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How credible is your raw data? What are the recent initiatives you
have taken to assess and improve the quality of data that is being fed
into your data warehouse? How much time it takes to reprocess your
data?
A product is only as good as the raw material it is derived from. The
same is true in case of data and information. Therefore it is of utmost
importance that good quality data enters the warehouse since it
directly impacts the quality of the information that comes of the
warehouse.
To achieve good quality data in the data warehouse, the data quality
problems have to be tackled at all phases. ETL and Data Staging is one
of the most crucial stages. It is a prime location for validating data
quality from source or auditing and tracking down data issues. It is
here that Outlier Analysis plays a very important role.
What’s new in Marlabs
Outlier Solution?
 Identifies outlier in data
load stage at file level
 Effectively saves response
time
 No need to wait for
analysis report
 Hierarchy drill reaches till
the raw data
File Level
File size
Record count
Length of record
Count of column
Marlabs Outlier Analysis methodology identifies data outliers at data load stage. Marlabs outlier’s algorithm
identifies anomalies using Advanced Statistical techniques and works in data warehousing framework.
Marlabs Offerings